About the Project
The cryosphere in the European Alps is expected to change substantially with global warming. Existing approaches to infer future snow conditions rely on physical models, either on regional climate models (RCMs) or snow-hydrological models, which are both computationally very intensive. To achieve a high-resolution output for such a large area as the Alps is almost impossible. In CliRSnow, empirical models derived from remote sensing (RS) are employed to provide an innovative and fast solution to increase the precision in future projections of snow cover from RCMs for the whole Alpine area. This will be achieved by correcting the bias in snow cover from RCMs and increasing the spatial resolution with RS snow cover data. Such an approach has now become feasible, because the data that forms its basis is on the verge of being sufficient in time (RS: MODIS time series since 2000) and space (RCM: EURO-CORDEX horizontal resolution at approx. 12.5km).
Publications on the project
Future snow cover in the alps: Using MODIS satellite observations to bias correct snow cover from the EURO-CORDEX regional climate model ensemble Matiu M, Petitta M, Notarnicola C, Premier V, Zebisch M (2019) Presentation/Speech Conference: International Mountain Conference | Innsbruck | 8.9.2019 - 12.9.2019 http://hdl.handle.net/10863/12805
Aiming at an alpine wide assessment of the temporal changes in the distribution of snow depth using quantile regression: results from a case study in the southern alps Matiu M, Petitta M, Notarnicola C, Zebisch M (2019) Presentation/Speech Conference: ICAM - International Conference on Alpine Meteorology | Riva del Garda | 2.9.2019 - 6.9.2019 http://hdl.handle.net/10863/12803
Present and future snow cover in the alps: Using MODIS satellite observations to evaluate and bias correct the EURO-CORDEX regional climate model ensemble Matiu M, Petitta M, Notarnicola C, Premier V, Zebisch M (2019) Presentation/Speech Conference: EGU General Assembly 2019 | Vienna | 7.4.2019 - 12.4.2019 http://hdl.handle.net/10863/12804
Datasets produced throughout the project
Matiu, M.; Jacob, A.; Notarnicola, C. Daily MODIS Snow Cover Maps for the European Alps from 2002 onwards at 250 m Horizontal Resolution Along with a Nearly Cloud-Free Version. Data 2020, 5, 1. https://doi.org/10.3390/data5010001
Matiu, M. Bias corrected and downscaled snow cover fraction from EURO-CORDEX RCMs for the Greater Alpine Region. https://doi.org/10.5281/zenodo.5266359
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Divides the whole Alps by elevation into high and low stations, irrespective of north-south or east-west orientation.
The main variability in snow is along elevation.
With 5 possible regions, the snow series in the Alps are divided by elevation, as well as gradients along north-south and east-west directions. These five regions were used in the analysis:
Elevation, the north-south divide, and the east-west gradient are the major drivers of snow variability.
This results in a consistent picture with the Alpine climate, where snow mirrors the temperature and precipitation patterns in the Alps.
The trends show the evolution of the snow height (HS) from 1971 to 2019.
The trends show the evolution of the snow cover duration (SCD) from 1971 to 2019.
2000-2020
The map shows the current situation of the snow cover duration (SCD) across the alps.
Average Mean Snow Cover Duration (SCD) based on cloud-filtered MODIS maps at 250m resolution and 19 years of observations from 2000-10-01 to 2019-09-30. The value represents snow covered days [0-365].
By opening the layer menu also the datasets shown in the future tab of this website can be visualized and compared.
RCP 2.6 2041-2070
The maps show different scenarios of future snow cover duration (SCD) across the Alps.
Annual Mean Snow Cover Duration (SCD) according to climate projections under the RCP 2.6 scenario from 2041 to 2070. The value represents snow covered days [0-365].
RCP: A Representative Concentration Pathway (RCP) is a greenhouse gas concentration (not emissions) trajectory adopted by the IPCC.
RCP 2.6 2071-2100
The maps show different scenarios of future snow cover duration (SCD) across the Alps.
Annual Mean Snow Cover Duration (SCD) according to climate projections under the RCP 2.6 scenario from 2071 to 2100. The value represents snow covered days [0-365].
RCP: A Representative Concentration Pathway (RCP) is a greenhouse gas concentration (not emissions) trajectory adopted by the IPCC.
RCP 8.5 2041-2070
The maps show different scenarios of future snow cover duration (SCD) across the Alps:
Annual Mean Snow Cover Duration (SCD) according to climate projections under the RCP 8.5 scenario from 2041 to 2070. The value represents snow covered days [0-365].
RCP: A Representative Concentration Pathway (RCP) is a greenhouse gas concentration (not emissions) trajectory adopted by the IPCC.
RCP 8.5 2071-2100
The maps show different scenarios of future snow cover duration (SCD) across the Alps.
Annual Mean Snow Cover Duration (SCD) according to climate projections under the RCP 8.5 scenario from 2070 to 2100. The value represents snow covered days [0-365].
RCP: A Representative Concentration Pathway (RCP) is a greenhouse gas concentration (not emissions) trajectory adopted by the IPCC.
RCP 2.6 vs RCP 8.5 2041-2070
The maps show different scenarios of future snow cover duration (SCD) across the Alps.
Annual Mean Snow Cover Duration (SCD) RCP 2.6 vs RCP 8.5 from 2041 to 2070. The value represents snow covered days [0-365].
RCP: A Representative Concentration Pathway (RCP) is a greenhouse gas concentration (not emissions) trajectory adopted by the IPCC.
RCP 2.6 vs RCP 8.5 2071-2100
The maps show different scenarios of future snow cover duration (SCD) across the Alps.
Annual Mean Snow Cover Duration (SCD) RCP 2.6 vs RCP 8.5 from 2071 to 2100. The value represents snow covered days [0-365].
RCP: A Representative Concentration Pathway (RCP) is a greenhouse gas concentration (not emissions) trajectory adopted by the IPCC.